GANs involve a unique architecture in which a pair of networks are trained simultaneously and in competition with each other. Training a robust GAN architecture is a non-trivial task due to problems like vanishing gradients and mode-collapse which may result in poor discrimination performance and synthetic samples with limited diversity [1]}, [2]}. The Wasserstein GANs (WGAN) architecture was introduced to remedy these issues using the Wasserstein distance as the loss function [3]}. The Conditional GAN (CGAN) is a specific class of GANs that involves the conditional generation of images by a generator model, resulting in new data observations with associated class labels [4]}. This modification to the GAN architecture permits learning the
distributions specific to each class label, producing samples for
both labels with higher quality.
